Photon Counting Histogram Expectation Maximization Algorithm for Characterization of Deep Sub-Electron Read Noise Sensors

Hendrickson, Aaron; Haefner, David P.

doi:10.1109/jeds.2023.3290106

Cited by 5 publications

(6 citation statements)

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“…Nevertheless, this approach can be regarded as a characterization method in its own right. 8 The idea behind this technique, similar to the PCH method, stemmed from the fact that the PCD exhibits periodic oscillations when the read noise is sufficiently small. This approach revolves around the analytical expression for the magnitude of the PCD Fourier transform given by…”

Section: Fourier Transform Methodsmentioning

confidence: 99%

“…Further improvements on μ are possible using autocorrelation. 8 The Fourier based method echos that of the PCH method in that it requires sufficiently small read noise to work. It also requires some implementation of peak detection and refines the initial estimates with nonlinear least squares.…”

Section: Fourier Transform Methodsmentioning

confidence: 99%

“…The Photon Counting Histogram Expectation Maximization (PCH-EM) algorithm (in review at the time of writing) is the latest iteration of methods for performing sensor characterization based on the PCD model. 8,9,17 It is the first technique devised to compute simultaneous maximum likelihood estimates for all four PCD parameters using only a single sample of data. This method was inspired from the fact that when the electron numbers associated with each observation are known, that is, we have the complete data (x, k) = {(x 1 , k 1 ), .…”

Section: Pch Expectation Maximization Algorithmmentioning

confidence: 99%

“…, (x n1 , k n1 )}, closed-form maximum likelihood estimators for each PCD parameter are easily derived. 8 While the electron numbers cannot be directly observed, this fact motivates a latent (hidden) variables model of estimation, which is what the general expectation maximization algorithm provides. As such, PCH-EM is a specific implementation of the general EM algorithm with the PCD being the underlying distribution to be estimated.…”

Section: Pch Expectation Maximization Algorithmmentioning

confidence: 99%

“…The additional structure observed in DSERN sensor data has led to the development of several new methods of conversion gain estimation, which leverage the additional structure to produce lower uncertainty estimates in comparison to the traditional PT method. [6][7][8] What is not clear in this body of research is that all of the proposed methods are derived from the same statistical model, which is valid for sensors with sub-electron and multi-electron read noise. Furthermore, while these newly proposed methods were designed to leverage the additional structure in data produced by DSERN capable devices, some show promise in characterizing sensors outside the DSERN regime; thus, serving as a general estimation procedure to supersede the legacy PT method.…”

Section: Introductionmentioning

confidence: 99%

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A comparative study of methods to estimate conversion gain in sub-electron and multi-electron read noise regimes

Hendrickson

Haefner

2023

Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXIV

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Of all sensor performance parameters, the conversion gain is arguably the most fundamental as it describes the conversion of photoelectrons at the sensor input into digital numbers at the output. Due in part to the emergence of deep sub-electron read noise image sensors in recent years, the literature has seen a resurgence of papers detailing methods for estimating conversion gain in both the sub-electron and multi-electron read noise regimes. Each of the proposed methods work from identical noise models but nevertheless yield diverse procedures for estimating conversion gain. Here, an overview of the proposed methods is provided along with an investigation into their assumptions, uncertainty, and measurement requirements. A sensitivity analysis is conducted using synthetic data for a variety of different sensor configurations. Specifically, the dependence of the conversion gain estimate uncertainty on the magnitude of read noise and quanta exposure is explored. Guidance into the trade-offs between the different methods is provided so that experimenters understand which method is optimal for their application. In support of the reproducible research effort, the MATLAB functions associated with this work can be found on the Mathworks file exchange.

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Section: Fourier Transform Methodsmentioning

confidence: 99%

Section: Fourier Transform Methodsmentioning

confidence: 99%

Section: Pch Expectation Maximization Algorithmmentioning

confidence: 99%

Section: Pch Expectation Maximization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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